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\begin{document}

\title{Does Tree Crown Architecture Differ by Tree Species? A test with NEON data}



\author[1]{Yiting Fan}%
\author[1,1]{Ethan R Cade}%
\author[1]{Conner W Channels}%
\author[2]{G Burch Fisher}%
\author[2]{Steven M Guinn}%
\author[1]{Christopher Hughes}%
\author[1]{Lydia R Nicolai}%
\author[2]{Andrew J Elmore}%
\author[1]{Brenden E Mcneil}%
\affil[1]{Department of Geology and Geography, West Virginia University}%
\affil[2]{University of Maryland Center for Environmental Science}%


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  \date{May 29, 2024}


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\begin{abstract}
Tree crown architecture, which we define as the 3-D arrangement and
orientation of leaves within a tree crown, influences the rates of
photosynthesis, evapotranspiration, and spectral reflectance that affect
tree and forest responses to climate change. As part of their adaptive
and acclimation strategies for responding to environmental variability,
trees are likely to differ in their tree crown architectures, but these
differences remain poorly described. We use measurements from 11
deciduous forest locations within the National Ecological Observatory
Network (NEON) to quantify traits that can define key dimensions of
variability in crown architecture. Specifically, we: (1) measure
seasonal trends in mean leaf angle (MLA) from tower-based time-lapse
photography, (2) quantify traits describing the density and distribution
of leaves in tree crowns from NEON Airborne Observation Platform (AOP)
LiDAR data, and (3) infer crown functioning from multi-scale data on
near-infrared reflectance of vegetation (NIRv), as obtained from
phenocams, the NEON AOP imaging spectrometer, and Harmonized Landsat
Sentinel-2 (HLS). From these data, we test for trait covariations (e.g.,
among MLA, the vertical distribution of plant area index, and the
seasonal peak of NIRv) that can suggest fundamental tradeoffs governing
how each species arranges and orients leaves in their crowns. In
describing how these crown architectural traits covary across the
diverse tree species and wide environmental gradients within 11 NEON
sites, we highlight implications for tree ecophysiology and remote
sensing-based studies on the interactions of trees, forests and climate
change.%
\end{abstract}%



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